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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 5 new columns ({'20220228', '18.1', '17', '71.76', '18'}) and 5 missing columns ({'electrical-meter-id', 'customer-id', 'hour', 'date', 'amount-of-consumption'}).

This happened while the csv dataset builder was generating data using

hf://datasets/andrewlee1807/Gyeonggi/xab.csv (at revision 264665bf10ad1c4efb94585c47119f20c040304e)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              18: int64
              20220228: int64
              17: int64
              18.1: int64
              71.76: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 794
              to
              {'electrical-meter-id': Value(dtype='int64', id=None), 'date': Value(dtype='int64', id=None), 'hour': Value(dtype='int64', id=None), 'customer-id': Value(dtype='int64', id=None), 'amount-of-consumption': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 5 new columns ({'20220228', '18.1', '17', '71.76', '18'}) and 5 missing columns ({'electrical-meter-id', 'customer-id', 'hour', 'date', 'amount-of-consumption'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/andrewlee1807/Gyeonggi/xab.csv (at revision 264665bf10ad1c4efb94585c47119f20c040304e)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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electrical-meter-id
int64
date
int64
hour
int64
customer-id
int64
amount-of-consumption
float64
1
20,200,801
4
1
38.74
1
20,200,801
18
1
69.74
1
20,200,801
22
1
69.51
1
20,200,801
23
1
70.92
1
20,200,802
5
1
61.08
1
20,200,802
6
1
62.67
1
20,200,803
13
1
222.38
1
20,200,804
2
1
74.83
1
20,200,804
14
1
220.58
1
20,200,804
20
1
117.12
1
20,200,806
1
1
69.03
1
20,200,806
4
1
62.13
1
20,200,806
6
1
45.19
1
20,200,806
12
1
216.91
1
20,200,806
20
1
90.36
1
20,200,808
5
1
32.37
1
20,200,808
14
1
75.33
1
20,200,809
11
1
74.21
1
20,200,809
17
1
21.72
1
20,200,809
23
1
55.03
1
20,200,810
7
1
43.63
1
20,200,810
17
1
152.01
1
20,200,810
22
1
83.43
1
20,200,810
24
1
64.72
1
20,200,811
1
1
56.42
1
20,200,811
2
1
54.48
1
20,200,811
12
1
103.25
1
20,200,812
7
1
55.74
1
20,200,812
9
1
104.66
1
20,200,812
19
1
63.21
1
20,200,813
1
1
51.22
1
20,200,813
15
1
104.66
1
20,200,813
16
1
206.83
1
20,200,813
19
1
125.8
1
20,200,813
20
1
137.08
1
20,200,814
10
1
166.82
1
20,200,814
17
1
52.3
1
20,200,814
24
1
40.06
1
20,200,815
23
1
54.19
1
20,200,816
4
1
23.59
1
20,200,816
19
1
63.21
1
20,200,817
9
1
72.05
1
20,200,818
6
1
47.16
1
20,200,818
7
1
70.51
1
20,200,818
12
1
240.04
1
20,200,818
20
1
133.1
1
20,200,818
22
1
81.98
1
20,200,818
23
1
58.39
1
20,200,819
12
1
229.13
1
20,200,819
17
1
215.4
1
20,200,820
4
1
48.93
1
20,200,820
20
1
119
1
20,200,820
22
1
75.22
1
20,200,821
4
1
25.94
1
20,200,821
22
1
71.61
1
20,200,821
24
1
50.66
1
20,200,822
14
1
75.1
1
20,200,823
2
1
46.3
1
20,200,823
14
1
89.19
1
20,200,824
4
1
44.34
1
20,200,824
5
1
34.78
1
20,200,824
6
1
47.33
1
20,200,824
17
1
225.66
1
20,200,824
24
1
51.36
1
20,200,825
2
1
49.1
1
20,200,825
3
1
49.32
1
20,200,825
4
1
49.16
1
20,200,825
7
1
59.73
1
20,200,826
2
1
52.3
1
20,200,826
9
1
188
1
20,200,826
20
1
99.29
1
20,200,827
1
1
74.92
1
20,200,827
5
1
61.6
1
20,200,828
12
1
238.64
1
20,200,828
14
1
231.94
1
20,200,829
3
1
49.91
1
20,200,829
17
1
81.5
1
20,200,829
23
1
50.95
1
20,200,830
2
1
48.1
1
20,200,830
9
1
72.81
1
20,200,830
19
1
78.76
1
20,200,831
6
1
46.83
1
20,200,831
7
1
62.74
1
20,200,831
13
1
224.64
1
20,200,831
18
1
209.4
1
20,200,831
23
1
60.04
1
20,200,901
1
1
49.17
1
20,200,901
2
1
50.07
1
20,200,901
3
1
48
1
20,200,901
4
1
47.31
1
20,200,901
5
1
48.26
1
20,200,901
6
1
48.08
1
20,200,901
7
1
61.44
1
20,200,901
8
1
98.28
1
20,200,901
9
1
200.21
1
20,200,901
10
1
214.38
1
20,200,901
11
1
215.67
1
20,200,901
12
1
223.29
1
20,200,901
13
1
217.1
1
20,200,901
14
1
217.97
End of preview.

Dataset Description

Gyeonggi dataset is 10,000 households based on the highest meter reading rate for all branches of the around in Gyeonggi Province, South Korea. For privacy reasons, the name of the household is not provided. We only provide the ID of the household.

Dataset Summary

This dataset en-compasses hourly records of building power consumption spanning approximately 1.9 years, ranging from January 1, 2021, to January 14, 2022.

electrical-meter-id date hour customer-id amount-of-consumption
7871 20201020 1 7871 4.25
7871 20201020 2 7871 4.12
7871 20201020 3 7871 4.08
7871 20201020 4 7871 4.03
7871 20201020 5 7871 4.09

Our experiment focuses on the total electricity consumption of a particular ID 6499


license: apache-2.0

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